Protein complexes are the cornerstones of most of the biological processes. Identifying protein complexes is crucial in understanding the principles of cellular organization with several important applications, including in disease diagnosis. Several computational techniques have been developed to identify protein complexes from protein-protein interaction (PPI) data (equivalently, from PPI networks). These PPI data have a significant amount of false positives, which is a bottleneck in identifying protein complexes correctly. Gene ontology (GO)-based semantic similarity measures can be used to assign a confidence score to PPIs. Consequently, low-confidence PPIs are highly likely to be false positives. In this paper, we systematically study the impact of low-confidence PPIs on the performance of complex detection methods using GO-based semantic similarity measures. We consider five state-of-the-art complex detection algorithms and nine GO-based similarity measures in the evaluation. We find that each complex detection algorithm significantly improves its performance after the filtration of low-similarity scored PPIs. It is also observed that the percentage improvement and the filtration percentage (of low-confidence PPIs) are highly correlated.The secondary and tertiary structure of a protein has a primary role in determining its function. Even though many folding prediction algorithms have been developed in the past decades - mainly based on the assumption that folding instructions are encoded within the protein sequence - experimental techniques remain the most reliable to establish protein structures. In this paper, we searched for signals related to the formation of [Formula see text]-helices. We carried out a statistical analysis on a large dataset of experimentally characterized secondary structure elements to find over- or under-occurrences of specific amino acids defining the boundaries of helical moieties. To validate our hypothesis, we trained various Machine Learning models, each equipped with an attention mechanism, to predict the occurrence of [Formula see text]-helices. The attention mechanism allows to interpret the model's decision, weighing the importance the predictor gives to each part of the input. The experimental results show that different models focus on the same subsequences, which can be seen as codes driving the secondary structure formation.Background Tumor purity is of great significance for the study of tumor genotyping and the prediction of recurrence, which is significantly affected by tumor heterogeneity. Tumor heterogeneity is the basis of drug resistance in various cancer treatments, and DNA methylation plays a core role in the generation of tumor heterogeneity. Almost all types of cancer cells are associated with abnormal DNA methylation in certain regions of the genome. The selection of tumor-related differential methylation sites, which can be used as an indicator of tumor purity, has important implications for purity assessment. At present, the selection of information sites mostly focuses on inter-tumor heterogeneity and ignores the heterogeneity of tumor growth space that is sample specificity. Results Considering the specificity of tumor samples and the information gain of individual tumor sample relative to the normal samples, we present an approach, PESM, to evaluate the tumor purity through the specificity difference methylation sites of tumor samples. Applied to more than 200 tumor samples of Prostate adenocarcinoma (PRAD) and Kidney renal clear cell carcinoma (KIRC), it shows that the tumor purity estimated by PESM is highly consistent with other existing methods. In addition, PESM performs better than the method that uses the integrated signal of methylation sites to estimate purity. Therefore, different information sites selection methods have an important impact on the estimation of tumor purity, and the selection of sample specific information sites has a certain significance for accurate identification of tumor purity of samples.Objective To investigate the relationship between self-reported osteoarthritis (OA) and reproductive factors in the Women's Health Initiative (WHI). Method We used multivariable logistic regression to study the association of self-reported OA and reproductive factors in the WHI Observational Study and Clinical Trial cohorts of 145 965 postmenopausal women, in a retrospective cross-sectional format. Results In our cohort, we observed no clinically significant associations between reproductive factors and OA given small effect sizes. The following factors were associated with statistically significant increased likelihood of developing OA younger age at menarche (p less then 0.001), history of hysterectomy [adjusted odds ratio (aOR) 1.013, 95% confidence interval (CI) 1.004-1.022, p = 0.04 vs no hysterectomy], history of unilateral oophorectomy (aOR 1.015, 95% CI 1.004-1.026, p less then 0.01 vs no oophorectomy), parity (aOR 1.017, 95% CI 1.009-1.026, p less then 0.001), ever use of oral contraceptives (aOR 1.008, 95% CI 1.001-1.016, p less then 0.01 vs never use), and current use of hormonal therapy (reference current users, aOR 0.951, 95% CI 0.943-0.959 for never users; aOR 0.981, 95% CI 0.972-0.989 for past users; global p less then 0.001). Age at menopause, first birth, and pregnancy were not associated with OA. Among parous women, no clear pattern was observed with number of pregnancies, births, or duration of breastfeeding in relation to OA. Conclusion Our study showed that reproductive factors did not have significant clinical associations with OA after controlling for confounders. This may be due to complex hormonal effects. Additional investigation is warranted in prospective cohort studies. The Women's Health Initiative is registered under ClinicalTrials.gov. Trial registration ID NCT00000611.The apigenin is a bioactive flavonoid mostly found in fruits and vegetables that possess various biological activities. The current study was performed to compare the biological potentials of sodium citrate-based (SC-SNPs) and apigenin-based (AP-SNPs) synthesized silver nanoparticles under the in vitro and in vivo conditions. The synthesized silver nanoparticles were physically and chemically characterized. The anticancer, pro-apoptotic, and their anti-bacterial activities were determined. Further, the mice trial was conducted to determine the possible toxic effects of the synthesized silver nanoparticles. The result of particle size analysis revealed the nanometer sizes of the SC-SNPs and AP-SNPs were about 95.5 and 93.94?nm, respectively. https://www.selleckchem.com/products/compound-e.html Both nanoparticles indicated pseudo-spherical shape, homogenous dispersion with an appropriate good degree of stability. However, the anticancer potential, pro-apoptotic effects and antibacterial activity of AP-SNPs were higher than that of SC-SNPs. Moreover, the mice trial indicated that AP-SNPs improved the liver function through modulation of liver enzymes, lipid peroxidation, and increase in the expression of antioxidant enzymes (SOD and GPx) as compared to the mice received AP-SNPs during 30?day experiment.